Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 44
Sivu 9
... training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired ...
... training . The training process proceeds as follows : a large number of patterns are chosen as typical of those which the machine must ultimately classify . This set of patterns is called the training set . The desired ...
Sivu 10
... training method in this case would use the training set to derive estimates of X1 and X2 . Suppose the training set consisted of Ni patterns belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 ...
... training method in this case would use the training set to derive estimates of X1 and X2 . Suppose the training set consisted of Ni patterns belonging to category 1 and N2 patterns belonging to cate- gory 2. Reasonable estimates for X1 ...
Sivu 89
... set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as being split into R blocks of D dimensions each . Each D - dimensional ...
... set Z of higher - dimensional vectors from the training set y . Each vector Z in Z is of RD dimensions ; it will be convenient to think of the RD dimensions of Z as being split into R blocks of D dimensions each . Each D - dimensional ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |